Summary of Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking For Mission-critical Applications, by Hassan El Alami and Danda B. Rawat
Reinforcement Learning-enabled Satellite Constellation Reconfiguration and Retasking for Mission-Critical Applications
by Hassan El Alami, Danda B. Rawat
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses a crucial gap in the literature on satellite constellation applications by investigating the impact of satellite failures on constellation performance and developing strategies for reconfiguration and retasking. The authors introduce a system modeling approach to analyze GPS satellite constellations, considering scenarios where satellite failures occur during critical operations. They also propose reinforcement learning (RL) techniques, including Q-learning, Policy Gradient, DQN, and PPO, to manage satellite constellations and mitigate the effects of reconfiguration and retasking following satellite failures. The results show that DQN and PPO achieve effective outcomes in terms of average rewards, task completion rates, and response times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make a group of satellites work better when some of them stop working. This is important because it can happen during critical missions. To figure this out, the authors created a way to model the satellite system and test different approaches to reconfigure and retask the remaining satellites. They also use special computer learning techniques called reinforcement learning to find the best solutions. The results show that some of these approaches work really well in certain situations. |
Keywords
» Artificial intelligence » Reinforcement learning
